Today's distributed information gathering systems for decision support are often designed and parameterized with fixed targets, fixed precision, fixed transmission intervals, and fixed decision models. Such systems are good for constant surveillance of a fixed phenomenon. For example, the NASA Solar and Heliospheric Observatory (SOHO) satellite launched in 1995 observes the Sun and the solar wind. SOHO takes photos of the Sun at various spectra at fixed time intervals (avg. 18 mins) and transmits the digitized signals to ground stations at a fixed resolution for scientific studies. Science models are then built with the parameters based on SOHO specifications. For example, a model to detect corona mass ejection from a sequence of photos of the Sun assumes SOHO specification of 18 min acquisition intervals and 512 by 512 pixel resolution. Studies and model-building are largely one-way with no possibility of feeding back instructions to the satellite in real time. Scientists thus cannot instruct SOHO to focus on a corner of the Sun for better observation of corona mass ejection, for example.
As new generations of information gathering systems evolve, sensors are better equipped with processing and communication capabilities to receive and process instructions. Those intelligent sensors can be instructed to auto-focus on the areas of interest and perform on-line retargeting. Such capabilities are crucial for observing fast-changing phenomena such as forest fires and volcano eruptions. Information collected can be used in real-time decisions for early warning and disaster relief. Nevertheless, the new generation sensors continue to have limitations in computational power, storage space, electricity usage, communication bandwidth and so on. These limitations pose constraints on the whole end-to-end information gathering and decision support system.
For time critical missions, the main objectives of the information gathering and decision support systems are:    1. Achieve the highest model prediction quality when all the information required is returned  and analyzed at the server (e.g. ground station or decision making assistant);    2. Achieve the highest model prediction quality at any time when data is only partially returned to the server.
The first objective is stemmed from traditional decision support systems that are built on acquiring complete sets of data. The second objective addresses the need of time urgency and emphasizes that decisions may be made based on partial information—the best prediction from partially available data. As new data streams in, the server system may refine its predictions continuously and adaptively.
There are many challenges to constructing a continuously adaptive decision support system. Due to various acquisition, processing, storage and transmission limitations, it may be impossible to acquire the entire collection of data at the highest resolution possible. Processing constraints pose difficulties in processing the data in time and transmitting the results to the decision maker. Potentially huge amounts of data (1 TB a day from Terra Satellite) are a burden to its storage, search and retrieval.
The next generation decision support system must achieve the above objectives given the limitations. Clearly the traditional approach is infeasible in most occasions. The new system must be adaptive in resource (processing, storage, transmission) consumption and only use resources to obtain maximal model prediction quality. The present invention is a proposed solution to the development of the next generation decision support system.